def test_percentile_early_stopping_strategy(self):
exp = get_branin_experiment_with_timestamp_map_metric(rate=0.5)
for i in range(5):
trial = exp.new_trial().add_arm(
arm=get_branin_arms(n=1, seed=i)[0])
trial.run()
exp.attach_data(data=exp.fetch_data())
"""
Data looks like this:
arm_name metric_name mean sem trial_index timestamp
0 0_0 branin 146.138620 0.0 0 0
1 0_0 branin 117.388086 0.0 0 1
2 0_0 branin 99.950007 0.0 0 2
3 1_0 branin 113.057480 0.0 1 0
4 1_0 branin 90.815154 0.0 1 1
5 1_0 branin 77.324501 0.0 1 2
6 2_0 branin 44.627226 0.0 2 0
7 2_0 branin 35.847504 0.0 2 1
8 2_0 branin 30.522333 0.0 2 2
9 3_0 branin 143.375669 0.0 3 0
10 3_0 branin 115.168704 0.0 3 1
11 3_0 branin 98.060315 0.0 3 2
12 4_0 branin 65.033535 0.0 4 0
13 4_0 branin 52.239184 0.0 4 1
14 4_0 branin 44.479018 0.0 4 2
Looking at the most recent fidelity only (timestamp==2), we have
the following metric values for each trial:
0: 99.950007 <-- worst
3: 98.060315
1: 77.324501
4: 44.479018
2: 30.522333 <-- best
"""
idcs = set(exp.trials.keys())
early_stopping_strategy = PercentileEarlyStoppingStrategy(
percentile_threshold=25, )
should_stop = early_stopping_strategy.should_stop_trials_early(
trial_indices=idcs, experiment=exp)
self.assertEqual(set(should_stop), {0})
early_stopping_strategy = PercentileEarlyStoppingStrategy(
percentile_threshold=50, )
should_stop = early_stopping_strategy.should_stop_trials_early(
trial_indices=idcs, experiment=exp)
self.assertEqual(set(should_stop), {0, 3})
# respect trial_indices argument
should_stop = early_stopping_strategy.should_stop_trials_early(
trial_indices={0}, experiment=exp)
self.assertEqual(set(should_stop), {0})
early_stopping_strategy = PercentileEarlyStoppingStrategy(
percentile_threshold=75, )
should_stop = early_stopping_strategy.should_stop_trials_early(
trial_indices=idcs, experiment=exp)
self.assertEqual(set(should_stop), {0, 3, 1})
def _evaluate_early_stopping_with_df(
early_stopping_strategy: PercentileEarlyStoppingStrategy,
experiment: Experiment,
df: pd.DataFrame,
):
"""Helper function for testing PercentileEarlyStoppingStrategy
on an arbitrary (MapData) df."""
metric_to_aligned_means, _ = align_partial_results(
df=df,
progr_key="timestamp",
metrics=["branin_map"],
)
aligned_means = metric_to_aligned_means["branin_map"]
decisions = {
trial_index: early_stopping_strategy.should_stop_trial_early(
trial_index=trial_index,
experiment=experiment,
df=aligned_means,
minimize=experiment.optimization_config.objective.minimize,
)
for trial_index in set(experiment.trials.keys())
}
return {
trial_index: reason
for trial_index, (should_stop, reason) in decisions.items()
if should_stop
}
def get_percentile_early_stopping_strategy(
) -> PercentileEarlyStoppingStrategy:
return PercentileEarlyStoppingStrategy(
percentile_threshold=0.25,
min_progression=0.2,
min_curves=10,
trial_indices_to_ignore=[0, 1, 2],
)
def test_percentile_early_stopping_strategy_validation(self):
exp = get_branin_experiment()
for i in range(5):
trial = exp.new_trial().add_arm(
arm=get_branin_arms(n=1, seed=i)[0])
trial.run()
trial.mark_as(status=TrialStatus.COMPLETED)
early_stopping_strategy = PercentileEarlyStoppingStrategy()
idcs = set(exp.trials.keys())
exp.attach_data(data=exp.fetch_data())
# Non-MapData attached
with self.assertRaisesRegex(ValueError, "expects MapData"):
early_stopping_strategy.should_stop_trials_early(
trial_indices=idcs, experiment=exp)
exp = get_branin_experiment_with_timestamp_map_metric(rate=0.5)
for i in range(5):
trial = exp.new_trial().add_arm(
arm=get_branin_arms(n=1, seed=i)[0])
trial.run()
# No data attached
should_stop = early_stopping_strategy.should_stop_trials_early(
trial_indices=idcs, experiment=exp)
self.assertEqual(should_stop, {})
exp.attach_data(data=exp.fetch_data())
# Not enough learning curves
early_stopping_strategy = PercentileEarlyStoppingStrategy(
min_curves=6, )
should_stop = early_stopping_strategy.should_stop_trials_early(
trial_indices=idcs, experiment=exp)
self.assertEqual(should_stop, {})
# Most recent progression below minimum
early_stopping_strategy = PercentileEarlyStoppingStrategy(
min_progression=3, )
should_stop = early_stopping_strategy.should_stop_trials_early(
trial_indices=idcs, experiment=exp)
self.assertEqual(should_stop, {})
def test_early_stopping_with_unaligned_results(self):
# test case 1
exp = get_branin_experiment_with_timestamp_map_metric(rate=0.5)
for i in range(5):
trial = exp.new_trial().add_arm(
arm=get_branin_arms(n=1, seed=i)[0])
trial.run()
trial.mark_as(status=TrialStatus.COMPLETED)
# manually "unalign" timestamps to simulate real-world scenario
# where each curve reports results at different steps
data = exp.fetch_data()
unaligned_timestamps = [0, 1, 4, 1, 2, 3, 1, 3, 4, 0, 1, 2, 0, 2, 4]
data.df.loc[data.df["metric_name"] == "branin",
"timestamp"] = unaligned_timestamps
exp.attach_data(data=data)
"""
Dataframe after interpolation:
0 1 2 3 4
timestamp
0 146.138620 NaN NaN 143.375669 65.033535
1 117.388086 113.057480 44.627226 115.168704 58.636359
2 111.575393 90.815154 40.237365 98.060315 52.239184
3 105.762700 77.324501 35.847504 NaN 48.359101
4 99.950007 NaN 30.522333 NaN 44.479018
"""
# We consider trials 0, 2, and 4 for early stopping at progression 4,
# and choose to stop trial 0.
# We consider trial 1 for early stopping at progression 3, and
# choose to stop it.
# We consider trial 3 for early stopping at progression 2, and
# choose to stop it.
early_stopping_strategy = PercentileEarlyStoppingStrategy(
percentile_threshold=50,
min_curves=3,
)
should_stop = early_stopping_strategy.should_stop_trials_early(
trial_indices=set(exp.trials.keys()), experiment=exp)
self.assertEqual(set(should_stop), {0, 1, 3})
# test case 2, where trial 3 has only 1 data point
exp = get_branin_experiment_with_timestamp_map_metric(rate=0.5)
for i in range(5):
trial = exp.new_trial().add_arm(
arm=get_branin_arms(n=1, seed=i)[0])
trial.run()
trial.mark_as(status=TrialStatus.COMPLETED)
# manually "unalign" timestamps to simulate real-world scenario
# where each curve reports results at different steps
data = exp.fetch_data()
unaligned_timestamps = [0, 1, 4, 1, 2, 3, 1, 3, 4, 0, 1, 2, 0, 2, 4]
data.df.loc[data.df["metric_name"] == "branin",
"timestamp"] = unaligned_timestamps
# manually remove timestamps 1 and 2 for arm 3
data.df.drop([22, 23], inplace=True)
exp.attach_data(data=data)
"""
Dataframe after interpolation:
0 1 2 3 4
timestamp
0 146.138620 NaN NaN 143.375669 65.033535
1 117.388086 113.057480 44.627226 NaN 58.636359
2 111.575393 90.815154 40.237365 NaN 52.239184
3 105.762700 77.324501 35.847504 NaN 48.359101
4 99.950007 NaN 30.522333 NaN 44.479018
"""
# We consider trials 0, 2, and 4 for early stopping at progression 4,
# and choose to stop trial 0.
# We consider trial 1 for early stopping at progression 3, and
# choose to stop it.
# We consider trial 3 for early stopping at progression 0, and
# choose not to stop it.
early_stopping_strategy = PercentileEarlyStoppingStrategy(
percentile_threshold=50,
min_curves=3,
)
should_stop = early_stopping_strategy.should_stop_trials_early(
trial_indices=set(exp.trials.keys()), experiment=exp)
self.assertEqual(set(should_stop), {0, 1})
def get_percentile_early_stopping_strategy() -> PercentileEarlyStoppingStrategy:
return PercentileEarlyStoppingStrategy(
percentile_threshold=0.25,
min_progression=0.2,
min_curves=10,
)
